Adaptive precision setting for cached approximate values
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Indexing Uncertainty of Continuously Moving Objects
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Managing uncertainty in sensor database
ACM SIGMOD Record
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Indexing continuously changing data with mean-variance tree
Proceedings of the 2005 ACM symposium on Applied computing
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Probabilistic spatial queries on existentially uncertain data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
HUGVid: handling, indexing and querying of uncertain geo-tagged videos
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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With the rapid development of technologies related to Ubiquitous Sensor Network (USN), sensors are being utilized in various application areas. In general, a sensor has a low computing capacity and power and keeps sending data to the central server. In this environment, uncertain data can be stored in the central server due to delayed transmission or other reasons and make query processing produce wrong results. Thus, this paper examines how to process uncertain data in ubiquitous sensor networks and suggests an efficient index, called UR-tree, for uncertain data. The index reduces the cost of update by delaying update in uncertainty areas. In addition, it solves the problem of low accuracy in search resulting from update delay by delaying update only for specific update areas. Lastly, we analyze the performance of UR-tree and prove the superiority of its performance by comparing its performance with that of R-Tree and PTI using various datasets.